Towards Data Ecosystems in Smart Cities:

Success Factors of Urban Data Spaces

Josh Haberkern

and Thomas Schäffer

Institute of Information Systems, Heilbronn University of Applied Sciences, Max-Planck-Straße 39, Heilbronn, Germany

Keywords: Data Governance, e-Government, Smart City, Success Factor, Urban Data Space, Urban Data Platform.

Abstract: In planning and designing public urban services, cities are increasingly relying on digital systems and data.

Urban Data Spaces represent the data ecosystem of a city or region, bringing together municipalities,

municipal companies, citizens, and businesses. They enable the development and management of data-driven

services and aim to combat siloed data storage and usage. The main goal of this research paper is to examine

the success factors for public sector stakeholders in creating and managing Urban Data Spaces. Using a multi-

method approach (literature analysis, expert interviews, focus groups, and survey), we identified, validated,

and quantified 23 success factors. The success factors were categorized into five dimensions: Platform Design,

Platform Governance, Technical Platform Design, Platform Management Capabilities, and Stakeholder

Involvement. Key findings are: A shared vision of an open and interoperable Urban Data Space, supported

by a Life Cycle Management enables public management to benefit from data-driven services and become

more sustainable. In addition, a cross-organizational data governance and strategy with a focus on the

development of data competence and data quality management form the foundation of those Data Ecosystems.

Based on the identified success factors, this article presents recommendations for scientists and practitioners.

1 INTRODUCTION

Planning and maintaining urban systems with a focus

on the common good is described by (Taylor, 1998)

as one of the historically essential goals of urban

planning. In the context of technological

development, cities and municipalities are pursuing

the goal of becoming smarter and more connected

(Albino et al., 2015) but also more liveable and

sustainable (Creutzig et al., 2019; EIP-SCC, 2016).

By facilitating data-driven and innovative urban

services, digital technologies can help cities’

managers to address these challenges and achieve

sustainable prosperity (Hamalainen, 2021). Data,

according to (Charalabidis et al., 2022), hereby is a

fundamental resource for the implementation of all

government activities, from regulation to public

service delivery. Municipal decision-makers believe

that its systematic use would lead to a significant

improvement in the quality of work and life, as well

as to greater security and better policymaking

(Schieferdecker et al., 2018). Innovation from data

https://orcid.org/0009-0008-1538-1199

https://orcid.org/0000-0001-8097-286X

arises especially when data from different data

sources and contextual data is combined and

analysed. An intermodal mobility service, for

example, requires timetable data from different

modes of transport, movement data from many

travellers and information about traffic jams,

disruptions on railway lines or major events (Otto &

Burmann, 2021). Urban Data Spaces, which represent

the data ecosystem of a city or region (Barns, 2018),

enable the development and operation of data-driven

services by the municipality, municipal companies or

third parties. In particular, Urban Data Spaces should

avoid the current problem of often siloed data storage

and use (Schlüter & Strelau, 2021), as well as enable

data sovereignty and data privacy (Creutzig et al.,

2019; Otto & Steinbuß, 2019; Tcholtchev et al.,

2018). In contrast, the current handling of data is

characterised by the fact that municipalities and

municipal enterprises do not make sufficient use of

their data sets; they are often neither combined with

data from other providers nor made available for

further use by third parties (Bagheri et al., 2021;

548

Haberkern, J. and SchÃd’ffer, T.

Towards Data Ecosystems in Smart Cities: Success Factors of Urban Data Spaces.

DOI: 10.5220/0012136200003541

In Proceedings of the 12th International Conference on Data Science, Technology and Applications (DATA 2023), pages 548-558

ISBN: 978-989-758-664-4; ISSN: 2184-285X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Barns, 2018; Cuno et al., 2019). Efforts towards

common data platforms and integrated urban services

are thus needed (Creutzig et al., 2019).

Schieferdecker et al. (2018, p. 102) as well

mention the need for "municipalities to share their

knowledge across regions and internationally in order

to learn from each other and benefit from the

experiences of others" as well as the creation of best

practices to provide orientation for municipalities.

The future of these ecosystems therefore depends

on whether they are effectively planned, designed,

and managed. Bagheri et al. (2021) developed

corresponding success and value creation factors for

Urban Data Spaces from the perspective of the

platform providers and formulate the development of

success factors for Urban Data Spaces from the

perspective of the demand side, the municipalities, as

an open research gap.

The objective of this paper is to identify the

aspects that stakeholders, especially in public

administration, regard as success factors for Urban

Data Spaces. Their different technological, societal,

and administrative dimensions will be discussed in

the context of this study. Accordingly, the research

questions (RQ) are:

 RQ1: What factors influence the success of an

Urban Data Space?

 RQ2: Which recommendations for action can

be derived for public sector stakeholders?

The structure of this paper is as follows: Firstly,

relevant terms related to the topic will be explained to

establish a common understanding. Next, the

multimethod research approach will be introduced.

The results of the study will then be presented and

explained through the analysis of the research

questions. In the discussion, the findings will be

critically reflected upon and the need for further

research will be identified.

2 THEORETICAL

BACKGROUND

This section introduces and defines the key terms as

they are used in this research. These are: Smart City,

Data Spaces, Urban Data Space, and Success Factor.

Smart City defines a city based on an intelligent

exchange of information between different

subsystems of a city and the analysis, use and

implementation of data in terms of services for

citizens and businesses (Gartner, 2011). According to

Albino et al. (2015), a smart city can be characterized

by four aspects: (1) the network infrastructure of a

city that enables politic al efficiency as well as social

and cultural development; (2) the business-led urban

development to promote urban growth; (3) the social

sustainability in form of social inclusion of different

city residents and social capital in urban

development; (4) the natural environment as a

strategic component for the future.

Data Space defines a level of abstraction that

provides a collection of data sources, services and

devices in one space (Franklin et al., 2005). These

data sources and services may be physically

distributed but are presented as a single entity through

the data space, making it easier for users to access and

use them, while data remains at their source. Based

on this explanation, the EU initiative Gaia-X defines

Data Spaces as a virtual data integration concept

(GAIA-X, 2021). Otto (2022) also refers to this

technological definition and specifically points out

the current use of the term: According to him, the

increasing use of the term in the business world has

led to the Data Space being understood as a form of

collaboration with data.

Urban Data Space is a special Data Space for a

Smart City. Schieferdecker et al. (2018) refer to an

Urban Data Space as one that contains the types of

data that may be relevant to the municipal

community, economy and policy space. Bagheri et al.

(2021) refer to Urban Data Spaces as a subset of

multi-sided digital platforms which enable the secure

and trustworthy exchange of data between different

user groups such as citizens, municipalities and

businesses. Bagheri et al. (2021) refer to the added

value of an Urban Data Space, which is supposed to

come by fostering the ecosystem of cities in such a

way that their (open) data (sources) are accessible to

others. Therefore, the Urban Data Space is seen as an

essential infrastructure for supporting data-driven

innovative services and to implement smart city

initiatives for a smart, sustainable and resilient city

(Barns, 2018; Cuno et al., 2019).

Success Factor refers to a cause or condition that

significantly contributes to the success or failure of a

company or project in business administration and

management research (Porter, 1998; Zhang and Li,

2010). Research on success factors is of great

importance for companies and public sectors as it

helps in developing strategies that increase the

likelihood of success. In information systems, the

term success factor refers to factors that influence the

success of information systems and their use in

companies (Fischer, 1993). Such factors may include

employee acceptance, data quality, integration into

business processes, and security and data protection

measures. Identifying and considering these factors is

Towards Data Ecosystems in Smart Cities: Success Factors of Urban Data Spaces

549

important to ensure that information systems are used

effectively and efficiently and contribute to the

success of the company. For our research, this means

that functional requirements with a high priority are

considered as success factors if they are crucial for

the success of a system (e.g., Urban Data Space).

3 RESEARCH METHODOLOGY

Figure 1 illustrates our multi-method research

approach to identify and validate the success factors

for Urban Data Spaces. The methods (literature

review, expert interview, focus group interviews, and

survey) were conducted between November 2022 and

March 2023.

In the first step, we conducted a literature review

to identify the basic dimensions and requirements of

Urban Data Spaces. We followed the approach

suggested by vom Brocke et al. (2009) and analyzed

the results according to Webster and Watson (2002).

We used ACM Digital Library, Google Scholar, IEEE

Xplore Digital Library, Science Direct, and Springer

Link to identify relevant literature. We used the

search term [("Smart City" OR "Smart Region")

AND ("Data Space" OR "Data Platform") AND

("success factors" OR "requirements" OR "functions"

OR "dimensions")] and analyzed the keywords and

abstract of each article. We considered a total of 44

publications focused on data platforms and

ecosystems in smart city contexts, as shown in Figure

1. From the results of the review, we derived a total

of 17 requirements using a requirements elicitation

process according to

ISO/IEC/IEEE

(2018) and

categorized them into five dimensions according to

Bagheri et al. (2021): Platform Vision, Platform

Governance, Technical Platform Design, Platform

Management Capabilities, and Stakeholder

Involvement.

This categorization refers to Data Spaces as a

whole, with a specific focus on urban spaces and the

public sector. In comparison, Nagel et al. (2021)

distinguish the "Building Blocks" only into

"Technical" and "Governance".

In the second step, we conducted interviews with

seven experts (Gläser & Laudel, 2010). The aim was to

examine the requirements for the urban data

infrastructure and prioritize the dimensions. The

experts are responsible for the urban data infrastructure

on a technological or strategic level in Darmstadt,

Frankfurt, Hamburg, Leipzig, Ulm and Vienna. We

used a semi-structured questionnaire with 21 questions

and analyzed the answers using qualitative content

analysis (Mayring, 2022). Each interview lasted

approximately one hour and was conducted online. To

participate, an Urban Data Space had to be in operation

in the respective city. As a result, it was determined that

the five dimensions were complete. The associated

priority per dimension was determined using a five-

point Likert scale (1 = lowest and 5 = highest priority)

per expert and presented as an arithmetic mean in the

form of a priority score (PS) in Table 1. We were also

Figure 1: Overview of the Research Methodology.

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550

able to identify ten additional requirements, so that

we now have a total of 27 requirements.

In the third step, we conducted an online survey

in Germany, Austria and Switzerland to determine the

practical relevance and priority of the 27

requirements (Stern et al., 2014).

We specifically invited experts from the urban

environment who are at different stages of

implementing Urban Data Spaces. In total, 26 people

participated in the survey, including 20 from public

administration, three from urban IT services, and

three from general IT consulting. The questionnaire

consisted of 27 questions that corresponded to the

requirements. Each requirement was rated using a

five-point Likert scale (1 = lowest priority and 5 =

highest priority). The result is the priority score (PS)

per requirement as an arithmetic mean.

In the fourth step, a focus group interview was

conducted with seven participants in order to evaluate

and finally prioritize the results from steps 2 and 3

with regard to their practical relevance (Littig &

Wallace, 1997; Merton, 1990). Participants work in

municipal/public administration, academia, or IT

companies and are responsible for topic areas related

to technological and/or strategic issues in the context

of urban data infrastructure. The focus group

interview lasted 75 minutes and was conducted

online. The evaluation revealed 23 success factors for

Urban Data Spaces.

In the fifth step, the results were made available.

In section 4, the 23 success factors according to RQ1

are presented in tables 2-6, structured according to the

dimensions or in descending order according to the

priority score (PS) determined, and then explained in

text. In section 5, twelve recommendations for action

according to RQ2 are drawn up based on the findings

from research steps 1-4.

The bibliographic data of the 44 publications as

well as the complete questionnaire of the expert

interviews and survey are available from the authors

upon request.

4 FINDINGS

This section presents the identified success factors in

the context of Urban Data Spaces according to the

research approach described in section 3.

The success factors are mapped to the five

dimensions of Bagheri et al. (2021), which are listed

in Table 1 in descending order of priority score (PS).

Table 1: Overview of Dimensions of Urban Data Spaces.

Dimension PS

D.1 Platform Vision 4,2

D.2 Platform Governance 3,8

D.3 Technical Platform Desi

n 3,6

D.4 Platform Management Capabilities 2,6

D.5 Stakeholder Involvement 2,2

To enable a better understanding of the ranking of

the success factors, the corresponding dimension is

explained briefly in each subsequent section. A table

is then presented in each case with the associated

success factors and their priority score by the experts.

Each success factor is then explained in detail and

backed up with the experts' opinions and experiences.

The success factors are illustrated in the order of the

priority score from highest to lowest. This is to ensure

that special attention is paid to the most important

success factors and that they are given appropriate

consideration in the context of Urban Data Spaces.

In total, then, five dimensions and their associated

success factors are explained and discussed in detail

in the following sections. This should help to develop

a comprehensive understanding of which factors are

of particular importance in the implementation of

Urban Data Spaces.

4.1 Platform Vision

The Platform Vision defines the purpose of a platform

or ecosystem that is essential for attracting user

groups (Bagheri et al., 2021; Schreieck et al., 2018).

Table 2 lists the five success factors of the platform

vision with priority score from the experts, which are

explained below.

Table 2: Success Factors of Platform Vision.

# Success Factor PS

D.1.1 Sustainabilit

4,8

D.1.2 Provisioning of Citizen Services 4,4

D.1.3 Data-Driven Public Management 4,3

D.1.4 Economic Development 4,0

D.1.5 Social Im

act 3,8

Sustainability (D.1.1) is a critical consideration

for cities, and many municipalities are recognizing

the potential of an Urban Data Space to help achieve

sustainability goals. Some cities are even positioning

themselves as "smart green cities," with municipal

administrations taking a leading role in promoting

ecological sustainability and climate resilience

through various use cases, such as mobility and

energy solutions. Additionally, it was suggested that

using an Urban Data Space for energy management

could lead to more sustainable and efficient use of

Towards Data Ecosystems in Smart Cities: Success Factors of Urban Data Spaces

551

resources, reducing carbon emissions and promoting

a greener urban environment.

Provisioning of Citizen Services (D.1.2) was

claimed by experts to be provided better and more

intelligent through Urban Data Spaces. Cities thereby

would be able to improve citizen services in general

and therewith involve citizens better in public

decisions by also providing participation tools and

methods as parts of an Urban Data Space.

Data-Driven Public Management (D.1.3) was

identified as a success factor, with experts

emphasizing the importance of offering and

maintaining data-driven services and applications for

internal administrative management purposes. A

Business Intelligence Infrastructure and respective

services were seen as driving acceptance and enabling

holistic decision making within public

administration. Urban use cases in the field of

analytics were also highlighted as potential areas for

improving cities' decision making. Examples of such

use cases include route optimization for urban waste

collection vehicles, measuring pedestrian frequencies

to improve the location of citizen offices, forecasting

the occupancy rate of schools, or energy management

in buildings as part of a digital twin.

Economic Development (D.1.4) was considered a

crucial factor for success, as per the experts. They

suggested involving local businesses and start-ups in

the ideation and development of use cases to improve

the economic ecosystem of cities. In addition, the use

of data-driven solutions and services could also

attract new businesses and investment to the city,

enhancing its economic competitiveness.

Furthermore, a focus on sustainable economic

development could be achieved through the

development of innovative and environmentally

friendly use cases within the Urban Data Space.

Social Impact (D.1.5) was identified as a further

success factor regarding the Platform Vision of an

Urban Data Space, particularly through the

implementation of use cases in Healthcare or Energy

Management. Experts stated that the use of an Urban

Data Space can contribute to better health outcomes

for citizens by providing data-driven insights into

healthcare needs and identifying areas for

improvement. For example, the European project

"BigMedilytics" uses an Urban Data Space to develop

innovative solutions for healthcare, such as

personalized medicine and predictive analytics for

disease prevention (Ruiz et al., 2018). Another

example is the "Healthy New Towns" initiative in the

UK, which uses an Urban Data Space to gather data

on the health and well-being of residents in new

housing developments (Watts et al., 2020). Overall,

the use of an Urban Data Space in the healthcare

sector has the potential to drive research and

innovation, improve patient outcomes, and promote

healthy living environments.

4.2 Platform Governance

The Platform Governance defines who makes the

respective decisions in the urban data ecosystem and

builds the necessary regulations (Bagheri et al.,

2021). Table 3 lists the four success factors of the

platform governance with priority score from the

experts, which are explained below.

Table 3: Success Factors of Platform Governance.

# Success Factor PS

D.2.1 Unified Data Governance and Strate

4,7

D.2.2 Openness, Transparency, Interoperabilit

4,6

D.2.3 Digital Sovereignty 4,4

D.2.4 Business and O

erating Model 2,8

Unified Data Governance and Strategy (D.2.1)

has been emphasized as a critical factor in building

successful urban data ecosystems: As mentioned by

the experts, Cities often rely on top-down, technically

separated, line management approaches, resulting in

data silos in separate departments. Due to this

distribution of responsibilities and resources, a

comprehensive organised data governance, as well as

corresponding responsibility structures and a role

management in dealing with data were deemed as

particularly necessary. The internal IT as well as

implementing departments and partners must be able

to rely on them to guarantee data integrity, data

quality and data excellence. Accordingly, the

overarching development of a data strategy was

mentioned as of particular importance to create a

common goal and big picture. Additionally,

elaborated data ethics concepts and automated data

integration processes were considered as essential for

efficient and long-term data provision.

Openness, Transparency, and Interoperability

(D.2.2) as foundation principles regarding Urban

Data Spaces were highlighted in various contexts and

parts of this research as a success factor. Cities must

rely on open and interoperable platforms and

ecosystems to use standardized products, services, or

interfaces. Data usage and publication must be

transparent within Public Administration across

different departments, to improve general data quality

and quantity. Open licenses and open data use are

particularly important and have proven to be a

success factor in different cities. Publishing Open

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552

Data and using Open-Source (Software)-Components

are helping cities achieve these goals.

Digital Sovereignty (D.2.3) was considered a

success factor, as municipalities and cities often

reported problems in an increasing dependence on

individual software or infrastructure providers,

leading to dependency in service quality, financial

aspects, and regulatory aspects, e.g., certain software

providers not complying with specific privacy or

security guidelines. Apart from so-called Vendor

Lock-ins, those aspects can be drawn from the

perspective of Data Ownership regulations developed

by cities. Cities are able to combat those challenges

through diversification in the used products, services

and providers as well as through the use of Open-

Source-Software.

Business and operating model (D.2.4) refers to

cities needs of aligning their business and operating

model to public management goals. Business models

regarding urban data usage are rarely based on data

monetization – as reported by the experts, no relevant

positive experience has been made e.g., through Data

Marketplaces. As stated in the research, until now

those Urban Data Spaces often have been just seen as

project-based, short term funded, work – city

stakeholders reported that this often led to problems.

An important success factor identified in regard to

urban data business and operating models for cities is

to see Urban Data Spaces as a municipal

infrastructure task inside the city budget, with

corresponding long term focused financial and

operation plans.

4.3 Technical Platform Design

The Technical Platform Design describes the

technical architecture and infrastructure behind

Urban Data Spaces (Bagheri et al., 2021). Table 4

lists the five success factors of the technical platform

Design with priority score from the experts, which are

explained below.

Table 4: Success Factors of Technical Platform Design.

# Success Factor PS

D.3.1 Data Privac

4,9

D.3.2 IT Securit

4,8

D.3.3 Technical Competence 4,5

D.3.4 Data Anal

tics 4,4

D.3.5 Standardization and Scalabilit

3,9

Data Privacy (D.3.1) was identified as an

important criterion. To ensure data privacy, experts

highlighted the need to improve and specify the data

sharing and release process and ensure high-quality

data classification. Different technical departments

are responsible for sharing and classifying data, such

as personal or infrastructure data, which should not be

released openly. Therefore, those specialized data

owners must be trained in data privacy regulations.

Additionally, data synthesis and anonymization are

also crucial for cities to meet the challenges and

requirements of data privacy. Experts viewed Data

Privacy as a positive aspect rather than a restraint, as

it would build citizens' confidence in their decision to

participate in the Urban Data Space.

IT Security (D.3.2) was also identified as an

important factor for cities to consider, as they often

rely on external partners in this field. However,

according to the experts, cities are increasingly

appreciating the value of resilient infrastructure and

services. Cross-city cooperation projects are being

implemented to share resources and build up security

audit levels regarding Urban Data Spaces.

Technical Competence (D.3.3) must be developed

and expanded within the public administration. Cities

must increase the technical competencies of their

employees through new hires and training, especially

in the areas of IT management, software development

and data science. In addition, public IT service

providers must also build up their technological

competencies in their services and employees.

Data Analytics (D.3.4) was deemed a success

factor as an additional layer to the current possibilities

that cities often use today - providing data as open

data on their website and only visualizing it. In

addition to the above-mentioned factor (D.1.3), the

use of internal data-driven applications in public

management to foster digital processes or improve

holistic decision-making is crucial. Analytics

methods can also be used to improve citizen services,

such as providing information dashboards.

Standardization and Scalability (D.3.5) activities

are also described as an essential aspect. Cities should

base their Urban Data Spaces on appropriate

reference models and standardizations, e.g., DIN

SPEC 91357, which is used by most cities in this

research. This allows modularity of the systems and

thus facilitates reproducibility and extensibility.

Therefore, standardized APIs need to be created

within the Urban Data Space and they should be

provided in a stable and reliable way. In addition, a

higher level of standardization in other areas,

including data models and APIs, is also needed to

enable extensibility and inter-municipal scalability.

The use of standardization and related activities can

ensure the development of efficient and reliable

Urban Data Spaces.

4.4 Platform Management Capabilities

Platform Management Capabilities describe the

Towards Data Ecosystems in Smart Cities: Success Factors of Urban Data Spaces

553

skills required to effectively manage and coordinate

an Urban Data Space (Bagheri et al. 2021), including

organizational change, new ways of working, change

management processes, modernized procurement

processes and laws, staff training, and establishing a

data culture to ensure high-quality data. Table 5 lists

the three success factors of the platform management

capabilities with priority score from the experts,

which are explained below.

Table 5: Success Factors of Platform Management

Capabilities.

# Success Factor PS

D.4.1 Cross-organisational Cooperation

Collaboration

4,7

D.4.2 Data Quality Management 4,4

D.4.3 Life Cycle Management 4,3

Cross-organizational Cooperation and

Collaboration (D.4.1) are of paramount importance

in managing and nurturing an ecosystem. Therefore,

the Urban Data Space must be given high priority in

cities' organizational management and recognized at

both operational and strategic levels, as mentioned by

the experts, to increase usage and acceptance of the

platform by different departments and promote its

growth. Another factor in ecosystem nurturing was

identified as having corresponding leadership within

the organization that drives digital and data use like

an evangelist or guru. This guru could be represented,

for example, by a mayor or a Chief Information

Officer.

Data Quality Management (D.4.2) was

considered crucial because citizens must rely on the

integrity and quality of data published by official

public agencies. To support the responsible data

owners in different departments, it is essential to

develop appropriate data retrieval and updating

guidelines that ensure regularity and high quality. In

addition to this, having a central quality control

mechanism for metadata and content-based control is

also necessary to maintain the accuracy and

consistency of data published in the Urban Data

Space.

Life Cycle Management (D.4.3) refers to

optimizing the entire lifespan of Urban Data Spaces.

These spaces are considered a part of urban

infrastructure and must have a corresponding Life

Cycle Management, including a long-term

operational concept, professional IT management,

and appropriate financing models. Urban Data Spaces

should be viewed as long-term infrastructural

investments, as stated in D.2.4, rather than one-time

funded projects. Experts have criticized that currently

concepts of Urban Data Spaces fail in some cities

because they are seen as one-time projects rather than

being considered with the long-term focused Life

Cycle Management mentioned.

4.5 Stakeholder Involvement

Stakeholder Involvement describes the extent to

which an Urban Data Space enables collaboration,

partnerships, and co-creation among different

stakeholders (Bagheri et al., 2021). Table 6 lists the

six success factors of the stakeholder involvement

with priority score from the experts, which are

explained below.

Table 6: Success Factors of Stakeholder Involvement.

# Success Factor PS

D.5.1 Boards and Committees 4,6

D.5.2 Inte

-Municipal Cooperation 4,5

D.5.3 Citizen-Involvement 4,3

D.5.4 Political Stakeholders 4,2

D.5.5 Cooperation on different

Government Levels

3,7

D.5.6 Joint further Development

of the Urban Data S

ace

3,3

Boards and Committees (D.5.1) formed at inter-

agency levels were deemed important in the success

of Urban Data Spaces. Implementing them on

strategic and regulatory levels, as well as working

groups on operational levels involving different city

stakeholders, were identified as a central success

factor in involving stakeholders and bringing various

advantages. These advantages including critical

perspectives, increased acceptance in different city

departments, and a joint enhancement of the platform

ecosystem.

Inter-municipal Cooperation (D.5.2) and

knowledge exchange, including sharing of

applications and services, was identified as a key

success factor for Urban Data Spaces. This includes

efforts towards replicability, resource savings, and

the recognition that "data does not end at city limits",

with regional cooperation playing a critical role in

achieving success according to experts.

Involving Citizens (D.5.3) was deemed a further

success factor, for example through participation

measures. Cities have had positive experiences with

regular meetings to promote new platform features

and gather feedback from citizen.

Political Stakeholders (D.5.4) play a crucial role

in decision-making processes in a city and involving

them is essential for the success of Urban Data

Spaces. Their support can lead to greater acceptance

of the platform ecosystem and better financial

DATA 2023 - 12th International Conference on Data Science, Technology and Applications

554

backing, as important decisions are often made on this

level.

Cooperation on different Government Levels

(D.5.5) is viewed as important, even though

municipalities bear the primary responsibilities in

Urban Data Spaces. Collaboration at the state or

European Union level allows for standardization

efforts, replicability, and participation in financial

support measures.

Joint further Development of the Urban Data

Space (D.5.6) was deemed important to allow for

participation from citizens, start-ups, scientific

institutions, and businesses. Cities often seek

cooperation to gather new ideas and implementations

to further develop an Urban Data Space, through

participation events like a Smart City Forum or

dedicated hackathons. Civic developers are allowed

to bring in their solutions and perspectives. Cities

often provide the data for those events and have had

positive experiences with both civic developers and

start-ups providing their services and ideas. Experts

mentioned that cities are often unable or unwilling to

be solely responsible for the ideation and

development of all use cases. Therefore, they value

this form of civic involvement and participation,

which leads to more and better applications and

services as part of the Urban Data Space.

5 DISCUSSION

In this section, the results of the previous section are

critically examined and reflected upon. In particular,

the identified success factors for an Urban Data Space

and their significance for public administration are

addressed. Critical aspects such as data protection are

also considered, and the importance of open and

interoperable platform ecosystems as well as a

suitable data governance structure are emphasized.

The aim of this section is to place the results of the

research in a broader context and to highlight further

implications for the development and operation of

Urban Data Spaces.

5.1 Opportunities and Challenges

Experts identify the platform vision as the highest

priority for the Urban Data Space in public

administration. Sustainability is considered a key

success factor and includes environmental

sustainability and climate resilience. The use of data-

driven services and applications in public

administration is important to increase the acceptance

and quality of Urban Data Spaces and to provide

better services for citizens. Openness, transparency

and interoperability are other success factors, while

business models to monetize data should not play a

visible role.

However, there are also challenges. These include

privacy concerns, vendor lock-in, the need for

comprehensive organized data governance,

specialized data owners, staff training, change

management processes, financial sustainability,

conflicts of interest, and ensuring equal participation

of all stakeholders. This research suggests that the

long-term sustainability and success of Urban Data

Spaces will require significant organizational,

financial, and managerial changes that could be

challenging for cities. Therefore, cities need to

carefully consider the long-term implications and

resource requirements before embarking on urban

data space projects. Other implementation challenges

could include:

Technical Difficulties: There could be technical

issues that impact the performance and scalability of

the platform. For example, this could be due to the

complexity of the data, the size of the data sets, and

the computing power required.

Legal and Regulatory Issues: There could be legal

issues affecting the use and management of the data,

particularly with regard to data privacy and data

security. Compliance with data privacy and security

regulations is critical to the success of the platform.

Lack of Support: there could be a lack of support

from the city government and policy makers

responsible for funding and operating the platform.

Without the necessary support and resources, it will

be difficult to successfully operate the platform.

Difficulties in Collaboration: there might be

difficulties in collaboration with other institutions and

organizations responsible for providing and using

data. Close collaboration and coordination between

all stakeholders are essential for the success of the

platform.

Acceptance Problems: there could be problems with

user acceptance of the platform. If the platform is not

user-friendly or does not provide the expected

benefits, it may not be used, which will affect the

success of the platform.

5.2 Implications for Practice

Urban Data Spaces can play an important role in

supporting urban development processes and

improving the quality of life of citizens if the

challenges associated with them are successfully

addressed.

Towards Data Ecosystems in Smart Cities: Success Factors of Urban Data Spaces

555

Our research has led to twelve recommendations

that public sector stakeholders should consider when

designing, implementing, and operating Urban Data

Spaces. These recommendations consider the main

findings of our research and differ from data spaces

in an industrial context. The recommendations for

action were derived from the expert interviews,

survey and focus group (see figure 1 from step 2 to

step 4) and are as follows:

1. A shared vision to achieve the goals of the

Urban Data Space is necessary to promote user

adoption.

2. The use of data-driven tools and services in

public administration promotes holistic

decision-making and leads to better services

for citizens. Furthermore, those also lead to

higher participation and acceptance inside

different public administration departments.

3. A cross-organizational data governance

structure and data strategy are the cornerstone

of successful Urban Data Spaces.

4. Openness, transparency and interoperability

are basic principles that enable

standardization, flexible extensibility and

modularity and reflect the Urban Data

Ecosystem.

5. Ensure digital sovereignty to avoid lock-in to

specific providers and continue to decide

ownership of city and citizen data.

6. Data protection is essential for acceptance and

trust and must be ensured, especially through

strong European data protection regulations.

7. Cities and public IT infrastructure partners

must develop their technical and

technological competencies.

8. Public administration must become more

flexible and open for the establishment and

operation of successful Urban Data Spaces.

9. Consider Urban Data Spaces as a long-term

municipal infrastructure task and not treat

them as a one-off project concept.

10. Involve civil society and start-ups in the

development of applications and services and

in the development of new use cases.

11. Inter-municipal collaboration enables

resource savings, exchange of ideas and

experiences, as well as replication and sharing

of applications and services.

12. Evaluation and review of the Urban Data

Space are important to ensure success and

effectiveness.

6 CONCLUSION

In conclusion, this research has examined the

essential success factors for Urban Data Spaces and

the challenges that cities face in creating and

managing such platforms. The research has

highlighted the importance of platform vision,

platform governance, technical design, platform

management capabilities, and stakeholder

involvement for the successful development and

sustainability of Urban Data Spaces.

By analyzing current literature and expert

opinions, this study has identified key factors that can

enable cities to create and manage effective Urban

Data Spaces, which can support evidence-based

decision-making, enhance citizen services, and

promote sustainable urban development.

This final section summarizes the limitations of

the work and provides recommendations for future

research in the field of Urban Data Spaces.

6.1 Limitations

This research on the topic of success factors of Urban

Data Spaces for public administration has some

limitations that should be taken into account when

interpreting the results.

First, the study was limited to the European region

and thus the results cannot be easily transferred to

other regions. In addition, the data was obtained from

a limited number of expert interviews, which could

limit the representativeness of the results.

Furthermore, only the success factors of the specific

Urban Data Space project were investigated and other

aspects such as political, social or economic factors

were not included. Finally, the limitations of the

methods used for data collection and analysis were

not explicitly discussed.

Therefore, it is advisable to look critically at the

results of the study and future research should

consider other aspects to gain a more comprehensive

understanding of the success factors of Urban Data

Spaces.

6.2 Future Research

Further future work could include a case study, based

on a practical implementation of the given success

factors in one selected city.

Additionally, in a practical context an evaluation

of current solutions and concepts in cities contrasted

to this success factors would be interesting. Resulting

from this work, important functions and new success

factors for Urban Data Spaces could then be included,

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implemented and evaluated. This framework could

then be expanded at an international level, and

differences between regions could be further

illuminated.

Moreover, there is the need to take a further look

into the success factors. Each success factor could be

presented and elaborated deeper. Therefore, e.g., the

development of a Data Governance Structure would

be important for city stakeholders and future research.

Cities and future research must further ensure the

security of urban data and infrastructure.

Additionally, examining the impact of emerging

technologies such as artificial intelligence or further

sustainability potentials on Urban Data Spaces could

be an interesting avenue for future research.

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